class_docstr: Beam search decoder for RNN-T model with biasing support.
  
  Args:
      model (RNNT): RNN-T model to use.
      blank (int): index of blank token in vocabulary.
      temperature (float, optional): temperature to apply to joint network output.
          Larger values yield more uniform samples. (Default: 1.0)
      hypo_sort_key (Callable[[Hypothesis], float] or None, optional): callable that computes a score
          for a given hypothesis to rank hypotheses by. If ``None``, defaults to callable that returns
          hypothesis score normalized by token sequence length. (Default: None)
      step_max_tokens (int, optional): maximum number of tokens to emit per input time step. (Default: 100)
      trie (list, optional): the prefix tree for TCPGen biasing
      biasing (bool, optional): If true, do biasing, otherwise use standard RNN-T support
code: def _get_generation_prob(self, trie):
      if len(trie[0].keys()) == 0:
          return True
      else:
          return False
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import RNNT
  from torchaudio.prototype.models.rnnt import TrieNode
label_desc: Use image node_id 4 for calling the RNNTBeamSearchBiasing obj's underlying member method code with example usage: obj._get_generation_prob(trie) and returns: True, False